Hi all,
I'm using graph-tool a lot and I usually perform multiple random
initializations only to choose, in the end, the solution with lowest entropy.
As each init is a separate and independent process, I was thinking to use
joblib to parallelize the process. However I noticed something weird. Here we
go:
>>> from joblib import delayed, Parallel
>>> import graph_tool.all as gt
# choose a graph
>>> g = gt.collection.data['football']
# set some variables, as the number of inits
>>> n_init = 3
>>> fast_tol = 1e-3
>>> beta = 1000
>>> n_sweep = 10
# define a function for sweep, this is essentially what is found in official
docs
>>> def fast_min(state, beta, n_sweep, fast_tol):
>>> dS = 1
>>> while np.abs(dS) > fast_tol:
>>> dS, _, _ = state.multiflip_mcmc_sweep(beta=beta, niter=n_sweep)
>>> return state
# test with standard python list comprehension, this works
>>> pstates = [gt.PPBlockState(g) for x in range(n_init)]
>>> pstates = [fast_min(state, beta, n_sweep, fast_tol) for state in pstates]
>>> selected = pstates[np.argmin([x.entropy() for x in pstates])]
>>> print(gt.modularity(g, selected.get_blocks()))
0.5986403881107808
# test with 'threading' backend in joblib
>>> pstates = [gt.PPBlockState(g) for x in range(n_init)]
>>> pstates = Parallel(n_jobs=3, prefer='threads')(delayed(fast_min)(state,
>>> beta, n_sweep, fast_tol) for state in pstates)
>>> selected = pstates[np.argmin([x.entropy() for x in pstates])]
>>> print(gt.modularity(g, selected.get_blocks()))
0.5926606505592532
# test with default backend in joblib
>>> pstates = [gt.PPBlockState(g) for x in range(n_init)]
>>> pstates = Parallel(n_jobs=3)(delayed(fast_min)(state, beta, n_sweep,
>>> fast_tol) for state in pstates)
>>> selected = pstates[np.argmin([x.entropy() for x in pstates])]
>>> print(gt.modularity(g, selected.get_blocks()))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-733a38606e1a> in <module>
3
4 selected = pstates[np.argmin([x.entropy() for x in pstates])]
----> 5 print(gt.modularity(g, selected.get_blocks()))
~/anaconda3/envs/experimental/lib/python3.8/site-packages/graph_tool/inference/modularity.py
in modularity(g, b, gamma, weight)
84 Q = libinference.modularity(g._Graph__graph, gamma,
85 _prop("e", g, weight),
---> 86 _prop("v", g, b))
87 return Q
88
~/anaconda3/envs/experimental/lib/python3.8/site-packages/graph_tool/__init__.py
in _prop(t, g, prop)
177 raise ValueError("Received orphaned property map")
178 if g.base is not u.base:
--> 179 raise ValueError("Received property map for graph %s (base:
%s), expected: %s (base: %s)" %
180 (str(g), str(g.base), str(u), str(u.base)))
181 return pmap._get_any()
ValueError: Received property map for graph <Graph object, undirected, with 115
vertices and 613 edges, 4 internal vertex properties, 2 internal graph
properties, at 0x7fd7032982e0> (base: <Graph object, undirected, with 115
vertices and 613 edges, 4 internal vertex properties, 2 internal graph
properties, at 0x7fd7032982e0>), expected: <GraphView object, undirected, with
115 vertices and 613 edges, 4 internal vertex properties, 2 internal graph
properties, at 0x7fd707c650d0> (base: <Graph object, undirected, with 115
vertices and 613 edges, 4 internal vertex properties, 2 internal graph
properties, at 0x7fd7067e3700>)
I cannot figure out why I get this error, as the g.base and selected.g.state
are the same. I understand this is not directly a graph-tool issue, rather a
joblib one, still I'd like to understand why I'm getting this error
Thanks
d
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